In order to enable an iCal export link, your account needs to have an API key created. This key enables other applications to access data from within Indico even when you are neither using nor logged into the Indico system yourself with the link provided. Once created, you can manage your key at any time by going to 'My Profile' and looking under the tab entitled 'HTTP API'. Further information about HTTP API keys can be found in the Indico documentation.

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Additionally to having an API key associated with your account, exporting private event information requires the usage of a persistent signature. This enables API URLs which do not expire after a few minutes so while the setting is active, anyone in possession of the link provided can access the information. Due to this, it is extremely important that you keep these links private and for your use only. If you think someone else may have acquired access to a link using this key in the future, you must immediately create a new key pair on the 'My Profile' page under the 'HTTP API' and update the iCalendar links afterwards.

Tutorials

From the CERN Large Hadron Collider to the ESO Extremely Large Telescope (ELT), National Instruments LabVIEW has played a critical part in the development, debugging, and deployment of these complex systems. In this tutorial, the concepts of LabVIEW and graphical programming will be introduced. Also, we will explore how to use LabVIEW to simplify common tasks in synchrotron development, such as EPICS protocol communication, as well as graphical programming on low-latency FPGA targets.

Machine learning and Deep Learning are quickly becoming powerful tools for solving complex modeling problems across a broad range of industries. The benefits of machine learning are being realized in applications everywhere, including predictive maintenance, health monitoring, financial portfolio forecasting, and advanced driver assistance. However, developing predictive models for signals is not a trivial task. In addition, there is an increasing need for developing smart sensor signal processing algorithms which can be either deployed on edge nodes or on the cloud.

In this session we will explore how you can use MATLAB for developing predictive models for real world sensor analytics using machine learning and deep learning workflows.

Tutorial Session III: Dive into Python

(13:30 ~ 15:00 Tuesday Oct. 16)

Speaker: Chi-Hung Weng (HongHuTech, Data Scientist)

Speaker will showcase some possible applications written in Python, ranging from web crawling, data cleaning, data visualization, to Machine Learning and Deep Learning. We then make an excursion to Deep Learning, where the following questions are to be answered: what is Deep Learning? what’s the theory behind it? What’s the difference between Deep Learning frameworks such as TensorFlow, Keras, MXNet and Pytorch?

Tutorial Session IV: Deep Learning

(15:20 ~ 17:00 Tuesday Oct. 16)

Speaker: Chi-Hung Weng (HongHuTech, Data Scientist)

Speaker will demonstrate & explain several deep learning applications in Computer Vision, including: image classification, object detection and semantic segmentation. Sample codes and datasets will be provided during the session.